TY - JOUR
T1 - Profit-Sensitive Spatial Scheduling of Multi-Application Tasks in Distributed Green Clouds
AU - Yuan, Haitao
AU - Bi, Jing
AU - Zhou, Meng Chu
N1 - Funding Information:
Manuscript received October 29, 2018; revised February 17, 2019; accepted April 1, 2019. Date of publication May 8, 2019; date of current version July 2, 2020. This article was recommended for publication by Associate Editor M. Dotoli and Editor S. Reveliotis upon evaluation of the reviewers’ comments. This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grants 61802015 and 61703011, in part by the Major Science and Technology Program for Water Pollution Control and Treatment of China under Grant 2018ZX07111005, and in part by the National Defense Pre-Research Foundation of China under Grants 41401020401 and 41401050102. This paper was presented at the IEEE CASE 2017, Xi’an, China. (Corresponding authors: Jing Bi and MengChu Zhou.) H. Yuan is with the School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China (e-mail: htyuan@bjtu.edu.cn).
Publisher Copyright:
© 2004-2012 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - An increasing number of organizations choose distributed green data centers (DGDCs) and use their infrastructure resources to deploy and manage multiple applications that flexibly provide services to users around the world in a cost-effective way. The dramatic growth of tasks makes it highly challenging to maximize the total profit of a DGDC provider in a market, where the revenue, price of power grid, solar radiation, wind speed, the maximum amount of energy, on-site air density, and the number of servers in DGDCs all vary with geographical sites. Different from existing studies, this paper designs a profit-sensitive spatial scheduling (PS3) approach to maximize the total profit of a DGDC provider by smartly scheduling all tasks of multiple applications to meet their response time constraints. PS3 can well utilize such spatial diversity of the above factors. In each time slot, the profit maximization for the DGDC provider is formulated as a constrained nonlinear program and solved by the proposed genetic-simulated-annealing-based particle swarm optimization. Real-life trace-driven simulation experiments demonstrate that PS3 realizes higher total profit and throughput than two typical task scheduling methods. Note to Practitioners-This paper investigates the profit maximization problem for a DGDC provider, while the average response time of all arriving tasks of each application is within their corresponding constraint. Existing task scheduling approaches fail to jointly consider the spatial variations in many factors, including the revenue, price of power grid, solar radiation, wind speed, the maximum amount of energy, on-site air density, and the number of servers in DGDCs. Consequently, they cannot schedule all tasks of multiple applications within their response time constraints in a profit-sensitive way. In this paper, a profit-sensitive spatial scheduling (PS3) method that tackles the drawbacks of previous approaches is presented. It is achieved by adopting a proposed genetic-simulated-annealing-based particle swarm optimization algorithm that solves a constrained nonlinear program. Simulation experiments prove that compared with two typical scheduling approaches, it increases the total profit and throughput. It can be readily realized and incorporated into real-life industrial DGDCs. The future work should improve the proposed method by analyzing the indeterminacy in green energy and the uncertainty in tasks.
AB - An increasing number of organizations choose distributed green data centers (DGDCs) and use their infrastructure resources to deploy and manage multiple applications that flexibly provide services to users around the world in a cost-effective way. The dramatic growth of tasks makes it highly challenging to maximize the total profit of a DGDC provider in a market, where the revenue, price of power grid, solar radiation, wind speed, the maximum amount of energy, on-site air density, and the number of servers in DGDCs all vary with geographical sites. Different from existing studies, this paper designs a profit-sensitive spatial scheduling (PS3) approach to maximize the total profit of a DGDC provider by smartly scheduling all tasks of multiple applications to meet their response time constraints. PS3 can well utilize such spatial diversity of the above factors. In each time slot, the profit maximization for the DGDC provider is formulated as a constrained nonlinear program and solved by the proposed genetic-simulated-annealing-based particle swarm optimization. Real-life trace-driven simulation experiments demonstrate that PS3 realizes higher total profit and throughput than two typical task scheduling methods. Note to Practitioners-This paper investigates the profit maximization problem for a DGDC provider, while the average response time of all arriving tasks of each application is within their corresponding constraint. Existing task scheduling approaches fail to jointly consider the spatial variations in many factors, including the revenue, price of power grid, solar radiation, wind speed, the maximum amount of energy, on-site air density, and the number of servers in DGDCs. Consequently, they cannot schedule all tasks of multiple applications within their response time constraints in a profit-sensitive way. In this paper, a profit-sensitive spatial scheduling (PS3) method that tackles the drawbacks of previous approaches is presented. It is achieved by adopting a proposed genetic-simulated-annealing-based particle swarm optimization algorithm that solves a constrained nonlinear program. Simulation experiments prove that compared with two typical scheduling approaches, it increases the total profit and throughput. It can be readily realized and incorporated into real-life industrial DGDCs. The future work should improve the proposed method by analyzing the indeterminacy in green energy and the uncertainty in tasks.
KW - Data center
KW - distributed clouds
KW - genetic algorithm (GA)
KW - green computing
KW - metaheuristic optimization
KW - particle swarm optimization
KW - simulated annealing (SA)
KW - task scheduling
UR - http://www.scopus.com/inward/record.url?scp=85074851565&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074851565&partnerID=8YFLogxK
U2 - 10.1109/TASE.2019.2909866
DO - 10.1109/TASE.2019.2909866
M3 - Article
AN - SCOPUS:85074851565
SN - 1545-5955
VL - 17
SP - 1097
EP - 1106
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
IS - 3
M1 - 8709791
ER -